Computational Neuroscience and Cognitive Modelling

Author: Britt Anderson
Publisher: SAGE
ISBN: 1446297373
Format: PDF, Kindle
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"For the neuroscientist or psychologist who cringes at the sight of mathematical formulae and whose eyes glaze over at terms like differential equations, linear algebra, vectors, matrices, Bayes’ rule, and Boolean logic, this book just might be the therapy needed." - Anjan Chatterjee, Professor of Neurology, University of Pennsylvania "Anderson provides a gentle introduction to computational aspects of psychological science, managing to respect the reader’s intelligence while also being completely unintimidating. Using carefully-selected computational demonstrations, he guides students through a wide array of important approaches and tools, with little in the way of prerequisites...I recommend it with enthusiasm." - Asohan Amarasingham, The City University of New York This unique, self-contained and accessible textbook provides an introduction to computational modelling neuroscience accessible to readers with little or no background in computing or mathematics. Organized into thematic sections, the book spans from modelling integrate and firing neurons to playing the game Rock, Paper, Scissors in ACT-R. This non-technical guide shows how basic knowledge and modern computers can be combined for interesting simulations, progressing from early exercises utilizing spreadsheets, to simple programs in Python. Key Features include: Interleaved chapters that show how traditional computing constructs are simply disguised versions of the spread sheet methods. Mathematical facts and notation needed to understand the modelling methods are presented at their most basic and are interleaved with biographical and historical notes for contex. Numerous worked examples to demonstrate the themes and procedures of cognitive modelling. An excellent text for postgraduate students taking courses in research methods, computational neuroscience, computational modelling, cognitive science and neuroscience. It will be especially valuable to psychology students.

Computational Models of Brain and Behavior

Author: Ahmed A. Moustafa
Publisher: John Wiley & Sons
ISBN: 1119159067
Format: PDF, ePub
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A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

Introduction to Connectionist Modelling of Cognitive Processes

Author: Peter McLeod
Publisher: Oxford University Press, USA
ISBN: 9780198524274
Format: PDF, Docs
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Describes the principles of connectionist modelling, and its application in understanding how the brain produces speech, forms memories, recognizes faces, and how intellect develops and deteriorates after brain damage.

Computational Modeling of Cognition and Behavior

Author: Simon Farrell
Publisher: Cambridge University Press
ISBN: 110710999X
Format: PDF, ePub, Mobi
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This book presents an integrated framework for developing and testing computational models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models.

Advances in Social Computing and Digital Education

Author: Fernando Koch
Publisher: Springer
ISBN: 3319520393
Format: PDF, ePub
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This book constitutes the refereed proceedings of the 7th International Workshop on Collaborative Agents Research and Development, CARE 2016, held in Singapore in May 2016 and Second International Workshop on Social Computing in Digital Education, SocialEdu 2016, held in Zagreb, Croatia, in June 2016. For CARE 2016 there were 4 papers selected out of 7 submissions, and for SocialEdu 5 papers were selected from 7 submissions. The 9 extended and revised full papers presented were carefully reviewed. The papers deal with topics like techniques of continuous monitoring, human behaviour analysis, recommendation systems, adjustment of education activities, intelligent content placement, and others.

Fundamentals of Neural Network Modeling

Author: Randolph W. Parks
Publisher: MIT Press
ISBN: 9780262161756
Format: PDF, ePub, Docs
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Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes.

Computational Explorations in Cognitive Neuroscience

Author: Randall C. O'Reilly
Publisher: MIT Press
ISBN: 9780262650540
Format: PDF, ePub, Mobi
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This text, based on a course taught by Randall O'Reilly and Yuko Munakata over thepast several years, provides an in-depth introduction to the main ideas in the computationalcognitive neuroscience.

MATLAB for Neuroscientists

Author: Pascal Wallisch
Publisher: Academic Press
ISBN: 0123838371
Format: PDF, Kindle
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MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. The first complete volume on MATLAB focusing on neuroscience and psychology applications Problem-based approach with many examples from neuroscience and cognitive psychology using real data Illustrated in full color throughout Careful tutorial approach, by authors who are award-winning educators with strong teaching experience

Computational Models of Cognitive Processes

Author: Julien Mayor
Publisher: World Scientific
ISBN: 9814458856
Format: PDF, Docs
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Computational Models of Cognitive Processes collects refereed versions of papers presented at the 13th Neural Computation and Psychology Workshop (NCPW13) that took place July 2012, in San Sebastian (Spain). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on models of cognitive processes. Contents:Language:Modelling Language — Vision Interactions in the Hub and Spoke Framework (A C Smith, P Monaghan and F Huettig)Modelling Letter Perception: The Effect of Supervision and Top-Down Information on Simulated Reaction Times (M Klein, S Frank, S Madec and J Grainger)Encoding Words into a Potts Attractor Network (S Pirmoradian and A Treves)Unexpected Predictability in the Hawaiian Passive (Ō Parker Jones and J Mayor)Difference Between Spoken and Written Language Based on Zipf's Law Analysis (J S Kim, C Y Lee and B T Zhang)Reading Aloud is Quicker than Reading Silently: A Study in the Japanese Language Demonstrating the Enhancement of Cognitive Processing by Action (H-F Yanai, T Konno and A Enjyoji)Development:Testing a Dynamic Neural Field Model of Children's Category Labelling (K E Twomey and J S Horst)Theoretical and Computational Limitations in Simulating 3- to 4-Month-Old Infants' Categorization Processes (M Mermillod, N Vermeulen, G Kaminsky, E Gentaz and P Bonin)Reinforcement-Modulated Self-Organization in Infant Motor Speech Learning (A S Warlaumont)A Computational Model of the Headturn Preference Procedure: Design, Challenges, and Insights (C Bergmann, L Ten Bosch and L Boves)Right Otitis Media in Early Childhood and Language Development: An ERP Study (M F Alonso, P Uclés and P Saz)High-Level Cognition:The Influence of Implementation on “Hub” Models of Semantic Cognition (O Guest, R P Cooper and E J Davelaar)Hierarchical Structure in Prefrontal Cortex Improves Performance at Abstract Tasks (R Tukker, A C Van Rossum, S Frank and W F G Haselager)Interactive Activation Networks for Modelling Problem Solving (P Monaghan, T Ormerod and U N Sio)On Observational Learning of Hierarchies in Sequential Tasks: A Dynamic Neural Field Model (E Sousa, W Erlhagen and E Bicho)Knowing When to Quit on Unlearnable Problems: Another Step Towards Autonomous Learning (T R Shultz and E Doty)A Conflict/Control-Loop Hypothesis of Hemispheric Brain Reserve Capacity (N Rendell and E J Davelaar)Action and Emotion:Modeling the Actor-Critic Architecture by Combining Recent Work in Reservoir Computing and Temporal Difference Learning in Complex Environments (J J Rodny and D C Noelle)The Conceptualisation of Emotion Qualia: Semantic Clustering of Emotional Tweets (E Y Bann and J J Bryson)A Neuro-Computational Study of Laughter (M F Alonso, P Loste, J Navarro, R Del Moral, R Lahoz-Beltra and P C Marijuán) Readership: Students and researchers in biocybernetics, neuroscience, cognitive science, psychology and artificial intelligence and those interested in neural models of psychological phenomena. Keywords:Cognitive Science;Computational Modeling;Psychology;Neural NetworksKey Features:An invaluable resource for researchers interested in neural models of psychological phenomenaEnables readers to catch up with a fast moving discipline by reading contributions that are typically published as journal articles only a couple of years laterOffers an overview of current computational models of cognitive processes in a single bookChapters are written by world-leading experts in the field

Principles of Computational Modelling in Neuroscience

Author: David Sterratt
Publisher: Cambridge University Press
ISBN: 1139500791
Format: PDF, Mobi
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The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.